This audit analyzes www.alanranger.com as a global entity using a five-pillar framework to assess AI-powered search visibility, entity recognition, and SERP behavior. Each pillar is scored 0-100 and assigned a RAG (Red/Amber/Green) status based on performance thresholds.

Data Sources: Scores are calculated using data from Google Search Console API (impressions, clicks, position, queries), Google Business Profile API (ratings, reviews, locations, service areas), schema markup validation, backlink CSV upload, and Trustpilot reviews snapshot.

Score Calculation: The GAIO score combines five weighted pillars: Authority (30%: Behaviour 40% + Ranking 20% + Backlinks 20% + Reviews 20%), Content/Schema (25%: Foundation schemas 30% + Rich Results 35% + Coverage 20% + Diversity 15%), Visibility (20%: average position mapping), Local Entity (15%: NAP consistency + knowledge panel + locations), and Service Area (10%: service areas count with NAP multiplier). Brand & Entity Overlay and AI Summary Likelihood are overlay metrics that do not affect the GAIO score but provide additional insights. Scores are normalized to 0-100 scale with thresholds: Green (70), Amber (40-69), Red (<40). Money pages behaviour is tracked separately and stored per audit, so you can see whether Authority improvements are coming from better CTR on commercial pages or from broader site-wide changes.

1. Visibility

Measurement:

• Primary: Average position from Google Search Console (1-40 mapped to 100-10)
• Formula: posScore = 100 - ((clampedPosition - 1) / 39) * 90 where clampedPosition is constrained to 1-40
• Visibility score = clamped posScore (0-100)
• Score range: 0-100

Data Sources:

• Google Search Console API: impressions, clicks, average position, CTR
• Real-time data from GSC API for the selected date range

Data Checked: Search Console impressions/clicks, average position, CTR, ranking distribution.

2. Authority (E-E-A-T)

What Authority Represents:

Authority is not "how good the content is", it's "how much the outside world and searchers trust and choose you". It approximates E-E-A-T using four ingredients:

• Behavioural signals ÔÇô do people click your results when they see them?
• Ranking strength ÔÇô how often do you show near the top of organic results?
• Off-site credibility ÔÇô backlinks from other domains (Phase 3)
• Reputation & reviews ÔÇô ratings and review volume across GBP + website

Measurement:

• Four-component model: Behaviour (40%), Ranking (20%), Backlinks (20%), Reviews (20%)
• Formula: authority = 0.4 * behaviourScore + 0.2 * rankingScore + 0.2 * backlinkScore + 0.2 * reviewScore
• Each sub-score is normalized to 0-100, then combined with weights above
• Score range: 0-100

2.1 Behaviour Score (40%):

• From Google Search Console: Only uses queries where average position Ôëñ 20
• Computes: ctrAll = CTR for all ranking queries, ctrTop10 = CTR for impressions where position Ôëñ 10
• Maps: ctrAll 0-5% ÔåÆ 0-100 (values >5% capped at 100), ctrTop10 0-10% ÔåÆ 0-100
• Combines: BehaviourScore = 0.5 * score(ctrAll) + 0.5 * score(ctrTop10)
• This stops long-tail / low-rank pages dragging the whole site down

2.2 Ranking Score (20%):

• From GSC, again only for position Ôëñ 20
• avgPos = impression-weighted average position
• top10Share = % of impressions where position Ôëñ 10
• posScore = map(avgPos, 1 ÔåÆ 100, 20 ÔåÆ 0) (clamped)
• top10Score = top10Share as a % (0-100)
• Combines: RankingScore = 0.5 * posScore + 0.5 * top10Score

2.3 Review Score (20%):

• Inputs: From GBP API (gbpRating 0-5, gbpReviewCount) and Trustpilot snapshot (siteRating 4.6, siteReviewCount 610)
• Per source: ratingScore = (rating/5)*100, countScore = min(reviewCount, 500)/500*100
• sourceScore = 0.6 * ratingScore + 0.4 * countScore
• Final: ReviewScore = 0.6 * gbpSourceScore + 0.4 * siteSourceScore
• If one source missing, falls back to the other; if both missing, uses neutral default (50)

2.4 Backlink Score (20%):

• Phase 3 ÔÇô using CSVs: refDomainCount, qualityDomains, qualityRatio
• countScore = map(refDomainCount, 0 ÔåÆ 0, N ÔåÆ 100) where N is cap (e.g. 150-200)
• qualityScore = qualityRatio * 100
• Combines: BacklinkScore = 0.5 * countScore + 0.5 * qualityScore
• Currently placeholder (50) until Phase 3 is implemented

Data Sources:

• Google Search Console API (CTR, position, impressions by position bucket)
• Google Business Profile API (ratings, review count)
• Trustpilot reviews (historic snapshot: rating 4.6, count 610)
• Backlink CSV upload (referring domains, quality metrics) - Phase 3

How to Improve Authority:

• Behaviour: Improve titles/meta descriptions on pages already in top 10-20. Focus on high-impression queries in GSC.
• Ranking: Push more core pages into top 10 (content refreshes, internal links, stronger schema).
• Reviews: Keep growing GBP and Trustpilot reviews; higher rating and more volume both help.
• Backlinks: Acquire more distinct linking domains, favouring sites with real traffic/authority (Phase 3).

3. Local Entity

Measurement:

• Primary method (when Business Profile data available): Base score from NAP consistency (0-100), with bonuses:
  • Knowledge panel detected: +10 points
  • At least one location: +5 points
  • Final score capped at 100
• Fallback method (no Business Profile data): Derived from search performance:
  • Formula: localEntity = 60 + 0.3 * (posScore - 50) + 0.2 * (ctrScore - 50)
  • Uses position score and CTR score from GSC
• Score range: 0-100

Data Sources:

• Ô£à Primary: Google Business Profile API: NAP consistency score, knowledge panel detection, locations count
• ÔÜá´©Å Fallback: Google Search Console API: position, CTR (when Business Profile data unavailable)

Data Checked: NAP consistency percentage, knowledge panel presence, locations count, LocalBusiness schema presence, entity recognition signals.

4. Service Area

Measurement:

• Primary method (when Business Profile data available):
  • Base score from service areas count: 0 areas = 0, 1 area = 12.5, 2 areas = 25, ..., 8+ areas = 100
  • Formula: baseScore = min(serviceAreasCount * 12.5, 100)
  • NAP consistency multiplier applied: serviceArea = baseScore * (napConsistencyScore / 100)
  • If NAP consistency is low, service area score is reduced proportionally
• Fallback method (no Business Profile data): Derived from Local Entity score (when Business Profile unavailable)
• Score range: 0-100

Data Sources:

• Ô£à Primary: Google Business Profile API: service areas count, NAP consistency score
• ÔÜá´©Å Fallback: Derived from Local Entity (when Business Profile data unavailable)

Data Checked: Service areas count from Business Profile, NAP consistency percentage, geographic coverage signals.

5. Content/Schema

Measurement:

• Weighted calculation based on four components: Foundation schemas, Rich Results, Coverage, and Diversity
• Formula: contentSchema = (Foundation × 30%) + (Rich Results × 35%) + (Coverage × 20%) + (Diversity × 15%)
• Foundation Schemas (30%): Organization, Person, WebSite, BreadcrumbList presence. Score = (types present / 4) ├ù 100
• Rich Results (35%): Article, Event, Course, FAQ, HowTo, VideoObject, Product, LocalBusiness, Review, ImageObject, ItemList eligibility. Score = (eligible types / 11) ├ù 100
• Coverage (20%): Pages with schema / total pages. Score = coverage percentage
• Diversity (15%): Number of unique schema types across site. Score = min((unique types / 15) ├ù 100, 100)
• Data Source: Schema audit API (crawls actual pages for JSON-LD markup)
• Uses real schema coverage from CSV-based audit (when available)
• Score range: 0-100

Data Sources:

• Schema Audit API: CSV-based domain crawl of all site URLs
• Schema markup detection and type counting
• Foundation schema presence detection (Organization, Person, WebSite, BreadcrumbList)
• Rich result eligibility detection (Article, Event, Course, FAQ, HowTo, VideoObject, Product, LocalBusiness, Review, ImageObject, ItemList)

Data Checked: Total pages scanned, pages with schema, schema coverage percentage, schema types present (Organization, Person, LocalBusiness, Article, Course, Event, FAQ, HowTo, etc.), missing schema types, rich result eligibility (Article, Event, FAQ, Product, LocalBusiness, Course, Review, HowTo).

6. Brand & Entity Overlay

What Brand Overlay Represents:

Brand & Entity Overlay measures how strongly your brand is recognized in search and how well entity signals support AI understanding. It combines branded search performance, review signals, and entity recognition. Note: This is an overlay metric and does not directly affect the GAIO score calculation.

Measurement:

• Brand Search Component (40% of brand search score):
  • Brand Query Share: % of impressions that are branded queries (30%+ share = 100 points)
  • Brand CTR: Click-through rate on branded queries (40%+ CTR = 100 points)
  • Brand Avg Position: Average position for branded queries (normalized 1-10 ÔåÆ 100-0)
  • Formula: brandSearchScore = 0.4 * shareScore + 0.3 * ctrScore + 0.3 * posScore
• Combined Score:
  • Formula: brandOverlay = 0.4 * brandSearchScore + 0.3 * reviewScore + 0.3 * entityScore
  • Label thresholds: <40 = Weak, 40-69 = Developing, ÔëÑ70 = Strong
• Score range: 0-100

Data Sources:

• Google Search Console API: Branded query identification, brand impressions, brand clicks, brand CTR, brand average position
• Google Business Profile API: Review score (reused from Authority pillar)
• Local Entity score: Entity recognition signals (reused from Local Entity pillar)

Data Checked: Brand query share (% of total impressions), branded CTR, average brand position, review signals, entity recognition strength.

7. AI Summary Likelihood

What AI Summary Likelihood Represents:

AI Summary Likelihood indicates how likely AI systems (like Google's AI Overviews) are to provide accurate, comprehensive summaries about your brand and content. It combines snippet readiness, visibility, and brand strength signals.

Measurement:

• Three-component model: Snippet Readiness (50%), Visibility (30%), Brand & Entity (20%)
• Formula: aiSummary = 0.5 * snippetReadinessScore + 0.3 * visibilityScore + 0.2 * brandScore
• Label thresholds: <50 = Low, 50-69 = Medium, ÔëÑ70 = High
• Uses same RAG bands as GAIO Score (ÔëÑ70 green, ÔëÑ50 amber, <50 red)
• Score range: 0-100

Data Sources:

• Snippet Readiness score: From snippet readiness gauge (FAQ/HowTo/Article blocks, schema, rich results)
• Visibility score: Current Visibility pillar score (average position from GSC)
• Brand score: Brand & Entity Overlay score

Data Checked: Snippet-friendly content blocks, schema markup for rich results, average search position, branded search performance, entity recognition.